Abstract
In conservation and improvement programs of bovine livestock, an important parameter is morphological assessment, which consist of scoring an animal attending to its morphology, and is always performed by highly-qualified staff.
We present in this paper a system designed to help in morphological assessment, providing a score based on a lateral image of the cow. The system consist of two main parts. First, a feature extractor stage is used to reduce the information of the cow in the image to a set of parameters (describing the shape of the profile of the cow). For this stage, a model of the object is constructed by means of point distribution models (PDM), and later that model is used in the searching process within each image, that is carried out using genetic algorithms (GAs). Second, the parameters obtained are used in the following stage, where a multilayer perceptron is trained in order to provide the desired assessment, using the scores given by experts for selected cows.
The system has been tested with 124 images corresponding to 44 individuals of a special rustic breed, with very promising results, taking into account that the information contained in only one view of the cow is not complete.
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References
Bishop, C.M., Hinton, G.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – their training and application. Comp. Vision and Image Understanding 61(1), 38–59 (1995)
Edmonson, A.J., Lean, I.J., Weaver, L.D., Farver, T., Webster, G.: A body condition scoring chart for holstein dairy cows. Journal of Dairy Science 72(1), 68–78 (1989)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
González, H.M., García, C.J., Macías, M., Gallardo, R., Acevedo, M.I.: Application of repeated GA to deformable template matching in cattle images. In: Perales, F.J., Draper, B.A. (eds.) AMDO 2004. LNCS, vol. 3179, pp. 134–145. Springer, Heidelberg (2004)
González, H.M., García, C.J., Macías, M., Gallardo, R., Álvarez, F.J.: A method for interactive shape detection in cattle images using genetic algoriths. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 694–701. Springer, Heidelberg (2007)
Goyache, F., Del Coz, J.J., Quevedo, J.R., López, S., et al.: Using artificial intelligence to design and implement a morphological assessment system in beef cattle. Animal Science 73, 49–60 (2001)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms 2e. John Wiley, Chichester (2004)
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González-Velasco, H.M., García-Orellana, C.J., Macías-Macías, M., Gallardo-Caballero, R., García-Manso, A. (2011). Application of Neural Networks to Morphological Assessment in Bovine Livestock. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_23
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DOI: https://doi.org/10.1007/978-3-642-23957-1_23
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